Logo Questions Linux Laravel Mysql Ubuntu Git Menu
 

df.loc causes a SettingWithCopyWarning warning message

The following line of my code causes a warning :

import pandas as pd

s = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
s.loc[-1] = [5,np.nan,np.nan,6]
grouped = s.groupby(['A'])
for key_m, group_m in grouped:
    group_m.loc[-1] = [10,np.nan,np.nan,10]

C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

According to the documentation this is the recommended way of doing, so what is happening ?

Thanks for your help.

like image 637
Anthony Lethuillier Avatar asked Jan 26 '17 09:01

Anthony Lethuillier


People also ask

What does DF Loc mean?

DataFrame. loc[] is a property that is used to access a group of rows and columns by label(s) or a boolean array.

How do you stop pandas from SettingWithCopyWarning?

One approach that can be used to suppress SettingWithCopyWarning is to perform the chained operations into just a single loc operation. This will ensure that the assignment happens on the original DataFrame instead of a copy. Therefore, if we attempt doing so the warning should no longer be raised.

What is SettingWithCopyWarning?

A SettingWithCopyWarning warns the user of a potential bug and should never be ignored even if the program runs as expected. The warning arises when a line of code both gets an item and sets an item. Pandas does not assure whether the get item returns a view or a copy of the dataframe.


1 Answers

The documentation is slightly confusing.

Your dataframe is a copy of another dataframe. You can verify this by running bool(df.is_copy) You are getting the warning because you are trying to assign to this copy.

The warning/documentation is telling you how you should have constructed df in the first place. Not how you should assign to it now that it is a copy.

df = some_other_df[cols]

will make df a copy of some_other_df. The warning suggests doing this instead

df = some_other_df.loc[:, [cols]]

Now that it is done, if you choose to ignore this warning, you could

df = df.copy()

or

df.is_copy = None
like image 117
piRSquared Avatar answered Sep 22 '22 20:09

piRSquared